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Analysis of short-term heart rate and diastolic period variability using a refined fuzzy entropy method

Overview of attention for article published in BioMedical Engineering OnLine, July 2015
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  • Above-average Attention Score compared to outputs of the same age (51st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (55th percentile)

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Title
Analysis of short-term heart rate and diastolic period variability using a refined fuzzy entropy method
Published in
BioMedical Engineering OnLine, July 2015
DOI 10.1186/s12938-015-0063-z
Pubmed ID
Authors

Lizhen Ji, Peng Li, Ke Li, Xinpei Wang, Changchun Liu

Abstract

Heart rate variability (HRV) has been widely used in the non-invasive evaluation of cardiovascular function. Recent studies have also attached great importance to the cardiac diastolic period variability (DPV) examination. Short-term variability measurement (e.g., 5 min) has drawn increasing attention in clinical practice, since it is able to provide almost immediate measurement results and enables the real-time monitoring of cardiovascular function. However, it is still a contemporary challenge to robustly estimate the HRV and DPV parameters based on short-term recordings. In this study, a refined fuzzy entropy (rFuzzyEn) was developed by substituting a piecewise fuzzy membership function for the Gaussian function in conventional fuzzy entropy (FuzzyEn) measure. Its stability and robustness against additive noise compared with sample entropy (SampEn) and FuzzyEn, were examined by two well-accepted simulation models-the [Formula: see text] noise and the Logistic attractor. The rFuzzyEn was further applied to evaluate clinical short-term (5 min) HRV and DPV of the patients with coronary artery stenosis and healthy volunteers. Simulation results showed smaller fluctuations in the rFuzzyEn than in SampEn and FuzzyEn values when the data length was decreasing. Besides, rFuzzyEn could distinguish the simulation models with different amount of additive noise even when the percentage of additive noise reached 60%, but neither SampEn nor FuzzyEn showed comparable performance. Clinical HRV analysis did not indicate any significant differences between the patients with coronary artery disease and the healthy volunteers in all the three mentioned entropy measures (all p > 0.20). But clinical DPV analysis showed that the patient group had a significantly higher rFuzzyEn (p < 0.01) than the healthy group. However, no or less significant difference was observed between the two groups in either SampEn (p = 0.14) or FuzzyEn (p = 0.05). Our proposed rFuzzyEn outperformed conventional SampEn and FuzzyEn in terms of both stability and robustness against additive noise, particularly when the data set was relatively short. Analysis of DPV using rFuzzyEn may provide more valuable information to assess the cardiovascular states than the other entropy measures and has a potential for clinical application.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 1 2%
Austria 1 2%
Unknown 51 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 11%
Researcher 6 11%
Student > Bachelor 5 9%
Student > Postgraduate 4 8%
Student > Master 4 8%
Other 10 19%
Unknown 18 34%
Readers by discipline Count As %
Engineering 15 28%
Computer Science 3 6%
Psychology 3 6%
Environmental Science 2 4%
Sports and Recreations 2 4%
Other 7 13%
Unknown 21 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 July 2015.
All research outputs
#13,440,839
of 22,815,414 outputs
Outputs from BioMedical Engineering OnLine
#338
of 824 outputs
Outputs of similar age
#123,885
of 263,437 outputs
Outputs of similar age from BioMedical Engineering OnLine
#8
of 20 outputs
Altmetric has tracked 22,815,414 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 824 research outputs from this source. They receive a mean Attention Score of 4.6. This one has gotten more attention than average, scoring higher than 56% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 263,437 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.